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Coordination Models and Technologies toward Self-Organising Systems

Andrea Omicini andrea.omicini@unibo.it

Alma Mater Studiorum—Universit` a di Bologna

CILC 2011

Universit` a degli Studi “Gabriele D’Annunzio” di Chieti e Pescara Pescara, 31st of August 2011

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 1 / 66

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

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Motivations

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 3 / 66

Motivations

Complexity & Coordination

Complex computational systems. . . . . . intelligent, knowledge-intensive, pervasive, self-organising systems could be seen as the dynamic ensemble of a large number of distributed components, heterogeneous in nature, structure and behaviour put together somehow so as to build up a coherent overall system behaviour What is “somehow”? This is the key issue in the research for abstractions, models, technologies and methodologies for the engineering of complex systems This is the issue of coordination models and languages [Papadopoulos and Arbab, 1998, Busi et al., 2001]

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Motivations

Evolution of Coordination Models I

Origins of coordination models and languages Coordination models originated in the context of closed and parallel systems E.g., generative communication [Gelernter, 1985] as a means to enable/promote parallel computations

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Motivations

Evolution of Coordination Models II

Coordination models and languages today After twenty-five years of literature on coordination models and

• languages. . .

. . . they are now conceived as the potential sources for the abstractions and the technologies around which complex computational systems can be designed and built ? How did this happen? In this talk, I will attempt to provide you with a possible explanation and a perspective – based on [Omicini and Viroli, 2011] –, focussing in particular on the role of Computational Logics (CL henceforth)

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Classical Coordination Models

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 7 / 66

Classical Coordination Models

Space-based Models I

Tuple-based models Tuple-based models [Rossi et al., 2001] represent the main class of space-based coordination models There, communication and coordination occur through a shared data space

as in the case of blackboard systems [Corkill, 1991]

A shared data space for communication, whose life is independent of the interacting components, is the conceptual basis for generative communication [Gelernter, 1985] As such, it represents the essential environment abstraction for the support of openness in distributed systems

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Classical Coordination Models

Space-based Models II

Persistent coordination abstraction The key idea of generative communication is a coordination abstraction persisting along with the messages exchanged This is the essential pre-requisite for a system where components may come and go at run-time. . . . . . and provides for time uncoupling, which makes it possible to conceive and design patterns of interaction that could survive the potential erraticism of component behaviour

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Classical Coordination Models

Space-based Models III

Middleware coordination abstraction The notion of permanent coordination abstraction mandates for coordination middleware, whose life is independent of the coordinating component’s life. . . . . . so that patterns of interaction could be enforced by suitably shaping the computational environment independently of the computational components

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Classical Coordination Models

Space-based Models & Computational Logics

Pioneers: Shared Prolog Blackboard models [Corkill, 1991] largely worked as an inspiration for space-based coordination with computational logics Brogi and Ciancarini pioneered the blackboard-based interpretation of LP through their Shared Prolog [Brogi and Ciancarini, 1991]

where dynamics of logic DB is interpreted as communication with in and out “replacing” assert and retract

In nuce, Shared Prolog already contains some of the main features of CL-based approaches to coordination

unification as the matching mechanism communication as/through a logic theory distributed knowledge partition as multiple distributed logic programs (KS)

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Classical Coordination Models

Origins of Tuple-based Models I

Linda The ancestor of all tuple-based models is Linda In Linda [Gelernter, 1985], components communicate and synchronise by exchanging tuples through a shared tuple space There, communication and coordination occur through a shared data space

communication via tuples coordination via space behaviour in response to coordination primitives

! Linda was first conceived to support parallel computation in closed systems—at least, with no apparent concern for open systems

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Classical Coordination Models

Origins of Tuple-based Models II

From closed to open systems Linda introduces an environment abstraction devoted to the management of the (agent) interaction space As a conceptual consequence, computation and coordination

conceived as the management of interaction [Wegner, 1997]

were to be

considered as two orthogonal dimensions of computer-based systems [Gelernter and Carriero, 1992] handled – that is, analysed, modelled, designed, programmed – in an independent way, by adopting suitable abstractions and mechanisms

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Basic Features of Linda-based Models I

Tuples A tuple is an ordered collection of possibly-heterogeneous knowledge chunks → Synchronisation based on the availability of tuples means essentially synchronisation based on the availability of structured knowledge of some sort → Tuple-based coordination is first of all knowledge-based coordination where tuple spaces are possibly interpreted as knowledge repositories

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Basic Features of Linda-based Models II

Associative access Tuple spaces are accessed associatively

queries specify tuple templates that match tuples based on their structure and the data they contain

→ Complete uncoupling in communication

information neither on the sender nor on the structure of the share space is required for a message to be received

Synchronisation possible over a partial representation of knowledge—the tuple template

a fundamental feature in all the contexts where information is often vague, inaccurate, incomplete, or partially specified—as is typical in knowledge-intensive systems

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Basic Features of Linda-based Models III

Logic tuple-space models Tuples as first-order logic (FOL) facts

Components coordinate through FOL tuples Unification for associative access to tuple in the space Same syntax for tuples and templates

Tuple spaces are FOL theories

the shared communication space can be interpreted as a logic-based knowledge repository used for component coordination each tuple space could be thought as the FOL theory representing some domain element relevant for component coordination → “semantic” interpretation of logic tuple space (in engineering process acceptation)

Examples

Shared Prolog [Brogi and Ciancarini, 1991] ReSpecT [Omicini and Denti, 2001]

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Essential Features of Linda-derived Models I

Two other features characterise tuple-based models as they descend from the original Linda ancestor distribution of the coordination abstractions expressiveness of the coordination abstractions respectively termed as [Busi et al., 2001] “reshaping the coordination media” “programming the coordination rules”

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Essential Features of Linda-derived Models II

Reshaping the coordination media Distribution is essential for any complex system In the same way as components of a distributed system are spread all

• ver the system topology, multiple tuple spaces fill the system

environment, providing for distributed coordination abstractions

JavaSpaces [Freeman et al., 1999] by Sun TSpaces [Wyckoff et al., 1998] by IBM

This paves the way toward pervasive coordination systems Also, expressing the environment topology in a distributed setting is essential for the coordination of local interaction as well as of mobile components

Lime [Murphy et al., 2006] Klaim [De Nicola et al., 1998]

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Essential Features of Linda-derived Models III

Programming the coordination rules The expressiveness of coordination media often needs to be tailored to the complexity and peculiarity of the specific coordinated system So, a number of Linda derivatives, e.g.

Law-Governed Interaction [Minsky and Ungureanu, 2000] MARS [Cabri et al., 2000] ReSpecT [Omicini and Denti, 2001]

focus on the programmability of the tuple space, so as to

make it possible to explicitly express the rules of coordination embed them within the coordination abstraction

There, arbitrarily-complex coordination policies can be in principle associated to each and every coordination medium, which could be individually programmed so as to embed either global or local coordination policies, as required by the specific coordinated systems

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Classical Coordination Models Tuple-based Models for Complex Systems Coordination

Essential Features of Linda-derived Models IV

Situatedness The ability to define arbitrarily-complex coordination policies and to embed them within the coordination media should be in principle coupled with the ability to capture and react to arbitrary environment events ! . . . otherwise, environment-based coordination would not be supported directly by the coordination medium This provides for the level of situatedness typically required by coordination in pervasive computational environments

Situated ReSpecT [Casadei and Omicini, 2009]

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Nature-inspired Coordination Models

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 21 / 66

Nature-inspired Coordination Models

Not Only Linda: The Case of Gamma I

Legacy beyond Linda: Gamma The other ancestor of space-based models is Gamma [Ban˘ atre et al., 2001] Not a derivative of Linda In Gamma, a shared coordination space is ruled by chemical-like laws defined by the programmers

thus, Gamma reminds the features of programmable tuple space models

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Nature-inspired Coordination Models

Not Only Linda: The Case of Gamma II

Source of inspiration for Gamma Analogous to the CHAM (Chemical Abstract Machine) model [Berry, 1992] Coordination in Gamma is conceived as the evolution of a space governed by chemical-like rules

globally working as a rewriting system

Gamma is a nature-inspired coordination model

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Nature-inspired Coordination Models

Nature-inspired Coordination Models I

Nature-inspired computing The main idea is to extract models and patterns from natural systems

• f any sorts, and apply them within computational contexts

Nature-inspired models includes neural networks, genetic algorithms, swarm intelligence, . . . Strict relationship between coordination and complexity of systems → nature-inspired models of coordination are of particular interest in the engineering of complex computational systems

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Nature-inspired Coordination Models

Nature-inspired Coordination Models II

Nature-inspired coordination A whole class of coordination models is inspired by the extraction of patterns from natural and social complex systems Nature-inspired coordination models are mostly driven by the idea that

working complex systems exist in the real world which we can observe so as to understand their basic principles and mechanisms, to abstract them, and to bring them within our artificial systems

Understanding the principles and mechanisms of coordination within complex natural systems → defining coordination models and technologies for complex artificial systems

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Nature-inspired Coordination Models

Field-based Coordination Models

Field-based coordination Field-based coordination models are inspired by the way masses and particles move and self-organise according to gravitational/electromagnetic fields [Mamei and Zambonelli, 2006] Typically, a pervasive coordination infrastructure generates and maintains computational force fields which are sensed & modified by agents moving through the fields, according to the field intensity and sort

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Nature-inspired Coordination Models

TOTA

Field-based coordination in TOTA In TOTA [Mamei and Zambonelli, 2004], computational force fields takes the form of distributed tuples Distributed tuples

are generated by both the active components and by the pervasive coordination infrastructure propagate across the environment drive the actions and motion of the component themselves—e.g. allowing two mobile agents to find each other in a dynamic network

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Nature-inspired Coordination Models

Stigmergic Coordination I

Origins of nature-inspired coordination models Historically, nature-inspired models of coordination are grounded in studies on the behaviour of social insects, like ants or termites The key concept there is stigmergy, introduced by [Grass´ e, 1959] as an explanation for the coordination observed in termites societies, where “The coordination of tasks and the regulation of constructions are not directly dependent from the workers, but from constructions themselves.” Namely, the notion of stigmergy generally refers to a set of coordination mechanisms mediated by the environment. . . . . . which leads to the emergent behaviours typical of self-organising systems

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Nature-inspired Coordination Models

Stigmergic Coordination II

Example: Ants In ant colonies, chemical substances – namely pheromone – act as environment markers for specific social activities Pheromones drive both the individual & the social behaviour of ants

by the way, similarly to what happens e.g. in TOTA

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Nature-inspired Coordination Models

Environment-based Coordination

Coordination through the environment Most of nature-inspired coordination models are characterised by the active role of the environment For instance, both field-based and stigmergic coordination are based

• n some notion of environment affecting the behaviour of coordinated

components by shaping the space of component interaction Generally speaking, environment-based coordination systematically adopts structured abstractions for shaping the environment of system components so as to govern their interactions [Ricci et al., 2005] So, environment-based coordination generalises for instance upon both field-based and stigmergic coordination

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Nature-inspired Coordination Models

Cognitive Stigmergy I

Beyond stigmergy Stigmergy concerns emergent coordination in societies composed by a large amount of ant-like, non-rational agents However, stigmergic patterns are can be observed also in the context

• f societies composed by cognitive / rational agents

[Omicini et al., 2004] In this context

modifications to the environment are often amenable of an interpretation in the context of a shared, conventional system of signs the interacting agents feature cognitive abilities that can be used in the stigmergy-based interaction

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Nature-inspired Coordination Models

Cognitive Stigmergy II

From signals to signs: Cognitive stigmergy The notion of cognitive stigmergy was introduced as a first generalisation of stigmergic coordination to enable social activities of cognitive agents [Ricci et al., 2007] Multiple-level coordination between heterogeneous components

• rdinary components perceive environment markers as mere signals and

react accordingly intelligent components can read them as signs, and behave according to their symbolic interpretation

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Nature-inspired Coordination Models

Tuples & Stigmergy

Tuple-based models as a computational specimen for stigmergy multiple tuple spaces physically/logically distributed in a computational system could be seen as the building blocks of the system’s environment—the “walls” upon which coordinating agents can read and write signs in the form of tuples [Omicini, 2011] suspensive coordination primitives could be used either by “stupid” processes to synchonise upon pre-determined tuple patterns, or by intelligent agents properly interpreting the symbolic content of tuples—as in the case of logic tuples, for instance [Denti and Omicini, 1999] → self-organising patterns based on either stigmergy or cognitive stigmergy can be in principle built upon tuple-based coordination models and technologies

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 33 / 66

Nature-inspired Coordination Models

Logic Tuples & Stigmergy

Logic symbols for stigmergy What if pheromones are represented by logic tuples in a tuple space? Non-logic (non-intelligent) agents could just react to pheromone, logic (intelligent) agents could also understand them and react appropriately . . . which fits the conceptual framework of cognitive stigmergy

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Coordination in Self-organising Systems

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 35 / 66

Coordination in Self-organising Systems

Self-organising Coordination I

A shared legacy Nature-inspired coordination models

e.g., chemical, field-based, and stigmergic coordination models

share a fundamental feature

they come from the core of complex natural self-organising systems

As such, they are seemingly the most intuitive sources for abstractions and mechanisms around which self-organising artificial systems could be designed and built

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Coordination in Self-organising Systems

Self-organising Coordination II

Towards self-organising coordination Generally speaking, self-organising coordination can be defined as the management of system interactions featuring self-organising properties . . . namely, where [Viroli et al., 2009]

interactions are local global desired effects of coordination appear by emergence

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 37 / 66

Coordination in Self-organising Systems

Self-organising Coordination III

The problem of self-organising coordination In most classical coordination models the environment is filled with coordination media enacting coordination laws that are

typically reactive (essentially) deterministic global

In self-organising systems coordination

patterns typically appear at the global level by emergence from probabilistic, time-dependent coordination laws based on local criteria

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Coordination in Self-organising Systems

Features of Self-organising Coordination

Required features of self-organising coordination models According to [Viroli et al., 2009], the required features of coordination models for self-organising systems are Topology & locality On-line character Time-dependency Probabilistic behaviour

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Coordination in Self-organising Systems

Topology & Locality

Coordination middleware Topology & locality mostly affect the nature of the coordination middleware The coordination media provided should

be associated to distributed locations mostly govern interaction among local components not be merely reactive to interaction instead, be enacted as always-running services able to adapt their coordinative behaviour at run time → as in the case of Lime, ReSpecT and TOTA, among the others

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Coordination in Self-organising Systems

Time-dependency & Probabilistic Behaviour I

Classical coordination models Apparently classical coordination models apparently address the issues

• f time-dependency and probabilistic behaviour in some way

For instance

tuple matching templates are returned in a non-deterministic way chemical laws are known to be probabilistic and time-dependent

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Coordination in Self-organising Systems

Time-dependency & Probabilistic Behaviour II

• Nonetheless. . .

On the one hand

non-determinism of classical tuple-based model is just a “don’t know” non-determinism instead, non-determinism in self-organising systems is typically stochastic → models like TOTA, SwarmLinda [Tolksdorf and Menezes, 2004] and StoKlaim [Bravetti et al., 2009] have introduced stochastic mechanisms within tuple-based coordination

On the other hand

classical chemical coordination models like Gamma and CHAM do not really reproduce chemical behaviours since they can express neither stochastic behaviours nor time-dependent coordination rules

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Coordination in Self-organising Systems

Chemical & Tuple-based Coordination I

Chemical tuple spaces As a result, a chemical tuple-space model and infrastructure have been defined [Viroli et al., 2010] . . . . . . that embodies all the typical features of self-organisation in natural chemical systems There, self-organisation could be achieved in two ways

either by means of the behaviour of an individual chemical tuple space (intra-space self-organisation)

• r by means of a suitable pattern of interaction among chemical tuple

spaces (inter-space self-organisation)

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Coordination in Self-organising Systems

Chemical & Tuple-based Coordination II

Chemical tuple-based coordination for pervasive systems Chemical tuple-based coordination is based on tuples evolving by mimicking chemical systems—that is, in terms of reaction and diffusion rules that apply to tuples modulo semantic match General-purpose chemical reactions inspired by population dynamics are exploited that involve chemical tuples Such reactions can be used in pervasive applications to enact spatial computing patterns of competition and gradient-based interaction In [Viroli et al., 2011] such a model is tested against a self-adaptive display infrastructure providing people nearby with several visualization services (advertisements, news, personal and social content)—more generally, for self-aware pervasive service ecosystems

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Coordination in Self-organising Systems

Tuple-based Models for KIE I

Toward knowledge-intensive environments (KIE) In the overall, suitably-extended tuple-based models provide a promising platform for the design and development of self-organising coordinated systems Nonetheless, knowledge-intensive application scenarios pose a huge challenge for tuple-based models There, the aforementioned benefits of tuple-based coordination in terms of knowledge-based coordination fade in front of the problems it induces in terms of syntax & (mostly) semantics

e.g., two tuples containing the same data may not match due to differences in the tuple structure e.g., two tuples representing the same information may not match based on a different syntax adopted

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Coordination in Self-organising Systems

Tuple-based Models for KIE II

Two lines of extension in the literature Exploiting tuple-based coordination within a middleware for KIE

e.g., [Tolksdorf et al., 2008] experiments with a tuple-based coordination within Semantic Web middleware e.g., [Nixon et al., 2008] survey similar approaches

Enhancing the tuple space abstraction with a semantic interpretation

e.g., [Nardini et al., 2010] extend tuple spaces with a description logic framework so as to equip each tuple, template, and operation over tuple spaces with a well-founded semantic interpretation

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Coordination in Self-organising Systems

Self-organising Semantic Coordination (SOSC) I

Adding semantics to tuple spaces Generalisation of the basic principles and mechanisms of coordination and self-organisation for application to knowledge-intensive environments Everything still based on tuples and tuple spaces Now equipped with a semantic interpretation → Exploring a notion of self-organising semantic coordination

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 47 / 66

Coordination in Self-organising Systems

Self-organising Semantic Coordination (SOSC) II

SOSC SOSC as the management of interactions in knowledge-intensive systems where

interactions are local and involve sharing and processing of knowledge the global desired effects of coordination over distributed knowledge appear by emergence and through self-organisation.

Coordination middleware – in particular, tuple-based ones – should be adopted to support self-organising semantic coordination

as in the case of eternally adaptive service ecosystems for pervasive computing [Viroli and Zambonelli, 2010].

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 48 / 66

Coordination in Self-organising Systems

Computational Logics & SOSC: An Experiment I

Semantic TuCSoN [Nardini et al., 2011] Based on logic tuples and tuple spaces Tuple centers equipped with a semantic interpretation In Semantic TuCSoN, a tuple centre is a three-space abstraction with three CL-based languages

Prolog ordinary tuples for communication ReSpecT specification tuples for behavior specification OWL DL TBoxes and ABoxes for ontology

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Coordination in Self-organising Systems

Computational Logics & SOSC: An Experiment II

Semantic tuple centres FOL for Communication (Prolog) FOL for Coordination (ReSpecT) DL for Semantics (OWL DL) Communication Coordination Semantics

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Challenges

Outline

1

Motivations

2

Classical Coordination Models Tuple-based Models for Complex Systems Coordination

3

Nature-inspired Coordination Models

4

Coordination in Self-organising Systems

5

Challenges Challenges for Coordination Challenges for Computational Logics The SAPERE Project

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 51 / 66

Challenges Challenges for Coordination

The Future of Coordination Models & Technologies I

Impact of coordination models Coordination models, languages, technologies and infrastructures are going to deeply impact on the engineering of complex systems Also in terms of methodologies and software processes and on related research as well Challenges for coordination A huge number of technical challenges are waiting for the development of coordination middleware and infrastructures Such challenges will put the effectiveness of coordination-based approaches to test against many complex, real-world application scenarios

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 52 / 66

Challenges Challenges for Coordination

The Future of Coordination Models & Technologies II

Some of the main issues for coordination in complex systems Integration of organisational and security models in the coordination setting Full development and testing of nature-inspired coordination models Definition of knowledge-oriented coordination models and languages embodying international standards Construction of light-weight coordination technologies for pervasive scenarios Design of rich coordination frameworks providing developers with tools for the engineering of the interaction space in complex computational systems . . .

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Challenges Challenges for Computational Logics

The Future of CL for Coordination of Complex Systems

Goals of computational logics Injecting intelligence pervasively within complex distributed system. . . . . . by providing models and technologies for coordinated components, coordination medium, communication languages and knowledge representation Providing a basis for formal verification of properties in complex systems Non-compositional composition: a vision Complex systems obtained by composing a number of (pervasively) distributed services and agents connected through coordination artifacts within knowledge-intensive environments. . . . . . most of which, logic-based ones

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Challenges The SAPERE Project

Self-aware Pervasive Service Ecosystems

SAPERE European Project FP7 – 2010-2013a

http://www.sapere-project.eu http://apice.unibo.it/xwiki/bin/view/SAPERE/

Under the hat of the Proactive Initiative AWARENESS

http://www.aware-project.eu/

Based on chemical coordination for pervasive computing LSA (Live Semantic Annotation), as chemical tuples representing individuals, components, services in pervasive scenarios, and triggering eco-laws governing self-organisation of pervasive services

aThis work has been supported by the EU-FP7-FET Proactive project

SAPERE – Self-aware Pervasive Service Ecosystems, under contract no.256873

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Conclusion

Thanks to. . .

Everybody here for listening Fabio Fioravanti for inviting me Enea & No` e for being here with me!

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Bibliography

Bibliography I

Ban˘ atre, J.-P., Fradet, P., and Le M´ etayer, D. (2001). Gamma and the chemical reaction model: Fifteen years after. In Calude, C. S., P˘ aun, G., Rozenberg, G., and Salomaa, A., editors, Multiset Processing. Mathematical, Computer Science, and Molecular Computing Points of View, volume 2235

• f LNCS, pages 17–44. Springer.

Berry, G. (1992). The chemical abstract machine. Theoretical Computer Science, 96(1):217–248. Bravetti, M., Latella, D., Loreti, M., Massink, M., and Zavattaro, G. (2009). Combining timed coordination primitives and probabilistic tuple spaces. In Kaklamanis, C. and Nielson, F., editors, Trustworthy Global Computing, volume 5474 of LNCS, pages 52–68. Springer. 4th International Symposium (TGC 2008), Barcelona, Spain, 3-4 November 2008, Revised Selected Papers. Brogi, A. and Ciancarini, P. (1991). The concurrent language, Shared Prolog. ACM Transactions on Programming Languages and Systems (TOPLAS), 13(1):99–123.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 57 / 66

Bibliography

Bibliography II

Busi, N., Ciancarini, P., Gorrieri, R., and Zavattaro, G. (2001). Coordination models: A guided tour. In [Omicini et al., 2001], chapter 1, pages 6–24. Cabri, G., Leonardi, L., and Zambonelli, F. (2000). MARS: A programmable coordination architecture for mobile agents. IEEE Internet Computing, 4(4):26–35. Casadei, M. and Omicini, A. (2009). Situated tuple centres in ReSpecT. In Shin, S. Y., Ossowski, S., Menezes, R., and Viroli, M., editors, 24th Annual ACM Symposium on Applied Computing (SAC 2009), Honolulu, Hawai’i, USA. ACM. Corkill, D. (1991). Blackboard systems. Journal of AI Expert, 9(6):40–47. De Nicola, R., Ferrari, G., and Pugliese, R. (1998). KLAIM: A kernel language for agent interaction and mobility. IEEE Transaction on Software Engineering, 24(5):315–330.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 58 / 66

Bibliography

Bibliography III

Denti, E. and Omicini, A. (1999). Designing multi-agent systems around a programmable communication abstraction. In Meyer, J.-J. C. and Schobbens, P.-Y., editors, Formal Models of Agents, volume 1760

• f LNAI, pages 90–102. Springer-Verlag.

ESPRIT Project ModelAge Final Workshop. Selected Papers. Freeman, E., Hupfer, S., and Arnold, K. (1999). JavaSpaces Principles, Patterns, and Practice: Principles, Patterns and Practices. The Jini Technology Series. Addison-Wesley Longman. Gelernter, D. (1985). Generative communication in Linda. ACM Transactions on Programming Languages and Systems, 7(1):80–112. Gelernter, D. and Carriero, N. (1992). Coordination languages and their significance. Communications of the ACM, 35(2):97–107. Grass´ e, P.-P. (1959). La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la th´ eorie de la stigmergie: Essai d’interpr´ etation du comportement des termites constructeurs. Insectes Sociaux, 6(1):41–80.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 59 / 66

Bibliography

Bibliography IV

Mamei, M. and Zambonelli, F. (2004). Programming pervasive and mobile computing applications with the TOTA middleware. In Pervasive Computing and Communications, pages 263–273. 2nd IEEE Annual Conference (PerCom 2004), Orlando, FL, USA, 14–17 March 2004. Proceedings. Mamei, M. and Zambonelli, F. (2006). Field-Based Coordination for Pervasive Multiagent Systems. Models, Technologies, and Applications. Springer Series in Agent Technology. Springer. Minsky, N. H. and Ungureanu, V. (2000). Law-Governed interaction: A coordination and control mechanism for heterogeneous distributed systems. ACM Transactions on Software Engineering and Methodology (TOSEM), 9(3):273–305. Murphy, A. L., Picco, G. P., and Roman, G.-C. (2006). Lime: A coordination model and middleware supporting mobility of hosts and agents. ACM Transactions on Software Engineering and Methodology, 15(3):279–328. Nardini, E., Omicini, A., Viroli, M., and Schumacher, M. I. (2011). Coordinating e-health systems with TuCSoN semantic tuple centres. Applied Computing Review, 11(2):43–52.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 60 / 66

Bibliography

Bibliography V

Nardini, E., Viroli, M., and Panzavolta, E. (2010). Coordination in open and dynamic environments with TuCSoN semantic tuple centres. In Shin, S. Y., Ossowski, S., Schumacher, M., Palakal, M., Hung, C.-C., and Shin, D., editors, 25th Annual ACM Symposium on Applied Computing (SAC 2010), volume III, pages 2037–2044, Sierre, Switzerland. ACM. Nixon, L., Simperl, E., Krummenacher, R., and Martin-recuerda, F. (2008). Tuplespace-based computing for the Semantic Web: A survey of the state-of-the-art. Knowledge Engineering Review, 23(2):181–212. Omicini, A. (2011). Agents writing on walls: Cognitive stigmergy and beyond. In Paglieri, F., Tummolini, L., Falcone, R., and Miceli, M., editors, The Goals of Cognition. Festschrift for Cristiano Castelfranchi. College Publications, London. Omicini, A. and Denti, E. (2001). From tuple spaces to tuple centres. Science of Computer Programming, 41(3):277–294.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 61 / 66

Bibliography

Bibliography VI

Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C., and Tummolini, L. (2004). Coordination artifacts: Environment-based coordination for intelligent agents. In Jennings, N. R., Sierra, C., Sonenberg, L., and Tambe, M., editors, 3rd international Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), volume 1, pages 286–293, New York, USA. ACM. Omicini, A. and Viroli, M. (2011). Coordination models and languages: From parallel computing to self-organisation. The Knowledge Engineering Review, 26(1). Special Issue for the 25th Years of the Knowledge Engineering Review. Omicini, A., Zambonelli, F., Klusch, M., and Tolksdorf, R., editors (2001). Coordination of Internet Agents: Models, Technologies, and Applications. Springer-Verlag. Papadopoulos, G. A. and Arbab, F. (1998). Coordination models and languages. In Zelkowitz, M. V., editor, The Engineering of Large Systems, volume 46 of Advances in Computers, pages 329–400. Academic Press.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 62 / 66

Bibliography

Bibliography VII

Ricci, A., Omicini, A., Viroli, M., Gardelli, L., and Oliva, E. (2007). Cognitive stigmergy: Towards a framework based on agents and artifacts. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, Environments for MultiAgent Systems III, volume 4389 of LNAI, pages 124–140. Springer. 3rd International Workshop (E4MAS 2006), Hakodate, Japan, 8 May 2006. Selected Revised and Invited Papers. Ricci, A., Viroli, M., and Omicini, A. (2005). Environment-based coordination through coordination artifacts. In Weyns, D., Parunak, H. V. D., and Michel, F., editors, Environments for Multi-Agent Systems, volume 3374 of LNAI, pages 190–214. Springer. 1st International Workshop (E4MAS 2004), New York, NY, USA, 19 July 2004. Revised Selected Papers. Rossi, D., Cabri, G., and Denti, E. (2001). Tuple-based technologies for coordination. In [Omicini et al., 2001], chapter 4, pages 83–109. Tolksdorf, R. and Menezes, R. (2004). Using swarm intelligence in Linda systems. In Omicini, A., Petta, P., and Pitt, J., editors, Engineering Societies in the Agents World IV, volume 3071 of LNCS, pages 49–65. Springer.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 63 / 66

Bibliography

Bibliography VIII

Tolksdorf, R., Nixon, L., and Simperl, E. (2008). Towards a tuplespace-based middleware for the Semantic Web. Web Intelligence and Agent Systems, 6(3):235–251. Viroli, M., Casadei, M., Montagna, S., and Zambonelli, F. (2011). Spatial coordination of pervasive services through chemical-inspired tuple spaces. ACM Transactions on Autonomous and Adaptive Systems, 6(2):14:1 – 14:24. Viroli, M., Casadei, M., Nardini, E., and Omicini, A. (2010). Towards a chemical-inspired infrastructure for self-* pervasive applications. In Weyns, D., Malek, S., de Lemos, R., and Andersson, J., editors, Self-Organizing Architectures, volume 6090 of LNCS, chapter 8, pages 152–176. Springer. 1st International Workshop on Self-Organizing Architectures (SOAR 2009), Cambridge, UK, 14-17 September 2009, Revised Selected and Invited Papers. Viroli, M., Casadei, M., and Omicini, A. (2009). A framework for modelling and implementing self-organising coordination. In Shin, S. Y., Ossowski, S., Menezes, R., and Viroli, M., editors, 24th Annual ACM Symposium on Applied Computing (SAC 2009), volume III, pages 1353–1360, Honolulu, Hawai’i, USA. ACM.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 64 / 66

Bibliography

Bibliography IX

Viroli, M. and Zambonelli, F. (2010). A biochemical approach to adaptive service ecosystems. Information Sciences, 180(10):1876–1892. Wegner, P. (1997). Why interaction is more powerful than algorithms. Communications of the ACM, 40(5):80–91. Wyckoff, P., McLaughry, S. W., Lehman, T. J., and Ford, D. A. (1998). T Spaces. IBM Journal of Research and Development, 37(3 – Java Techonology):454–474.

Omicini (Universit` a di Bologna) Coordination Models toward SOS CILC 2011, 31/8/2011 65 / 66

Coordination Models and Technologies toward Self-Organising Systems

Andrea Omicini andrea.omicini@unibo.it

Alma Mater Studiorum—Universit` a di Bologna

CILC 2011

Universit` a degli Studi “Gabriele D’Annunzio” di Chieti e Pescara Pescara, 31st of August 2011

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